Surrogate Gradient-Based Medical Image Classification Using Spike Neural Network

Abstract

The human brain, an unparalleled paradigm for efficient signal processing, fuels the quest to design brain-like architectures in addressing real-world challenges. Among mimicked computational models, Spike Neural Networks (SNNs) have emerged notable in information processing due to their distinct resemblance to brain-inspired computing, with distinguishing features from conventional methodologies. In this paper, we introduce an innovative surrogate gradient-based technique tailored for medical image classification. Specifically, our approach propels a gradient-based SNN framework with the explicit objective of achieving proficient classification of breast cancer images. Employing the SNN architecture, our results reached an accuracy level of 92.52%. The voltage-driven spike behavior and spike patterns have also been apprehended to support the model's efficacy. This outcome highlights the latent potential of SNNs in achieving precise medical image classification. The framework we present signifies substantial advancements in comprehending the brain-inspired attributes of SNNs, with the way for streamlined and accurate diagnostic solutions in medical imaging.